Cognitive, Behavioral, and Autonomic Correlates of Mind Wandering and Perseverative Cognition in Major Depression

UCLA UCLA Previously Published Works

Title Cognitive, behavioral, and autonomic correlates of mind wandering and perseverative cognition in major depression.

Permalink https://escholarship.org/uc/item/2n44085c

Journal Frontiers in neuroscience, 8(JAN)

ISSN 1662-4548

Authors Ottaviani, Cristina Shahabi, Leila Tarvainen, Mika et al.

Publication Date 2014

DOI 10.3389/fnins.2014.00433

Peer reviewed

eScholarship.org Powered by the California Digital Library University of California ORIGINAL RESEARCH ARTICLE published: 05 January 2015 doi: 10.3389/fnins.2014.00433 Cognitive, behavioral, and autonomic correlates of mind wandering and perseverative cognition in major depression

Cristina Ottaviani 1,2*, Leila Shahabi 3, Mika Tarvainen 4,5, Ian Cook 6, Michelle Abrams 6 and David Shapiro 6

1 IRCCS Santa Lucia Foundation, Rome, Italy 2 ENPlab, Department of Psychology, Sapienza University of Rome, Rome, Italy 3 Department of Medicine, University of California, Los Angeles, Los Angeles, CA, USA 4 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland 5 Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland 6 Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles CA, USA

Edited by: Autonomic dysregulation has been hypothesized to play a role in the relationships between Yoko Nagai, University of Sussex, psychopathology and cardiovascular risk. An important transdiagnostic factor that has been UK associated with autonomic dysfunction is perseverative cognition (PC), mainly present in Reviewed by: Major Depressive Disorder (MDD) in the form of rumination. As the ability to adaptively Mattie Tops, VU University Amsterdam, Netherlands let our mind wander without ruminating is critical to mental health, this study aimed to Jessica Eccles, Brighton and Sussex examine the autonomic concomitants of functional vs. dysfunctional intrusive thoughts Medical School, UK in MDD. Ambulatory heart rate (HR) and variability (HRV) of 18 MDD subjects and *Correspondence: 18 healthy controls were recorded for 24 h. Approximately every 30 min during waking Cristina Ottaviani, ENPlab, hours subjects reported their ongoing thoughts and moods using electronic diaries. Department of Psychology, Sapienza University of Rome, via dei Marsi Random regression models were performed. Compared to controls, MDD subjects were 78, 00185 Rome, Italy more often caught during episodes of PC. In both groups, PC required more effort to e-mail: [email protected] be inhibited and interfered more with ongoing activities compared to mind wandering (MW) (ps < 0.0001). This cognitive rigidity was mirrored by autonomic inﬂexibility, as PC was characterized by lower HRV (p < 0.0001) compared to MW. A worse mood was reported by MDD patients compared to controls, independently of their ongoing cognitive process. Controls, however, showed the highest mood worsening during PC compared to being on task and MW. HRV during rumination correlated with self-reported somatic symptoms on the same day and several dispositional traits. MDD subjects showed lower HRV during sleep, which correlated with hopelessness rumination. Results show that PC is associated with autonomic dysfunctions in both healthy and MDD subjects. Understanding when spontaneous thought is adaptive and when it is not may clarify its role in the etiology of mood disorders, shedding light on the still unexplained association between psychopathology, chronic stress, and risk for health.

Keywords: perseverative cognition, rumination, mind wandering, heart rate, heart rate variability, ecological momentary assessment, ambulatory monitoring, major depression disorder

INTRODUCTION tended to precede mind wandering but mind wandering itself In the past decades, a growing interest in difﬁculties staying “in was not associated with later mood. Other authors, however, the moment” as a hallmark of Major Depressive Disorder (MDD) suggest that mind wandering may be not only a consequence has rapidly developed. The relationship between the tendency of but also a cause of low mood. Killingsworth and Gilbert (2010) the mind to wander and depression has been hypothesized to be analyzed experience samples from 2250 participants conclud- bi-directional. Several studies provide evidence of mind wandering that “a wandering mind is an unhappy mind.” Stawarczyk ing as a consequence of depressive state. For example, Smallwood et al. (2013) provided further evidence for the involvement of et al. (2009) experimentally manipulated mood and showed that mind wandering in predicting subsequent levels of momentary negative relative to positive mood reduced the amount of atten- negative affect. Moreover, several studies suggest an association tional commitment to the task probably by enhancing the focus between dysphoria and enhanced mind wandering. Murphy et al. on task irrelevant personal concerns. Poerio et al. (2013) used (2013) supported this hypothesis by manipulating the occur- a 7-day experience sampling technique showing that sadness rence of mind wandering in participants with varying levels of

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self-reported dysphoria. Smallwood et al. (2007) showed that in recurrent depression, suggesting that during the course of recur- dysphoric individuals mind wandering led to greater decoupling rent depression hypothalamic-pituitary-adrenal axis activation from task-relevant processing and greater physiological arousal, may become less responsive to subtle emotional experiences as indexed by higher heart rate (HR). in natural setting. Another ecological study of MDD patients Consistent with these results, interventions aimed to enhance measured prefrontal brain activity using electroencephalography control over mind wandering, such as mindfulness-based cogni- (EEG) at baseline and subsequent rumination during the follow- tive therapy (MBCT; Teasdale, 1999)havebeenshowntobeeffec- ing week (Putnam and McSweeney, 2008). Results showed that tive in preventing relapses in depression (Williams and Kuyken, only in MDD patients and not in healthy controls, less prefrontal 2012). Also, training aimed at increasing executive control (e.g., neural activity was associated with higher rumination and lower Siegle et al., 2014) or working memory capacity (Onraedt and self-esteem. Koster, 2014)havebeenshowntobebeneficialinreducinga To our knowledge, this is the first study to combine ecological well-established antecedent for depression, i.e., ruminative think- momentary assessment with simultaneous ambulatory HR and ing. In fact, by reducing mind wandering, these therapeutic variability (HRV) recording in MDD. This is surprising consider- approaches are effective because they target the ideal context for ing that patients suffering from MDD show profound autonomic rumination to occur (e.g., Marchetti et al., 2014), the latter being a dysfunctions characterized by a decrease in HRV (e.g., Udupa vulnerability factor for the onset, maintenance, and recurrence of et al., 2007; Koschke et al., 2009; Wang et al., 2013). Moreover, MDD (e.g., Nolen-Hoeksema et al., 2008). Contrary to what hap- HRV has been found to be a significant predictor of treatment pens during depressive rumination, mind wandering can facilitate response and remission from MDD (Chambers and Allen, 2002; a series of positive outcomes, such as creative problem solving, Jain et al., 2014), and HRV biofeedback seems a promising tool prospection, or relief from monotonous and boring activities (see for the treatment of MDD (Karavidas et al., 2007). Smallwood and Schooler, 2014 for a recent review). Based on our previous findings in healthy individuals In the present study, we aimed at investigating if mind wander- (Ottaviani et al., 2013), we hypothesized that mind wandering ing per se is associated with a series of maladaptive consequences, fails to serve its adaptive function, and turns into a risk factor such as mood worsening and autonomic imbalance or if it does for health whenever it becomes rigid and inflexible. As rumi- so only when it takes the form of rumination about the past or native and worrisome thoughts show different content but no worry about the future. To the best of our knowledge, this ques- differences in appraisals and strategies (Watkins et al., 2005), tion was directly addressed only in a previous laboratory study we collapsed them into one category under the umbrella term that we conducted on a sample of healthy participants (Ottaviani of perseverative cognition (PC) (see Brosschot et al., 2010). et al., 2013). Here, our main purpose is to further investigate this We expect that, both in MDD and healthy subjects, PC would issue in patients with a diagnosis of MDD in a more ecologi- be associated with higher levels of cognitive (higher efforts to cal setting. Ecological momentary assessment has been suggested inhibit), behavioral (interference with ongoing activities), and as the best way to study depression: first, it helps avoiding the autonomic (lower HRV) inflexibility compared to mind wander- recall bias toward negative memories that characterizes MDD; ing and being focused on a task. Based on our hypothesis that second, it facilitates generalization to real life; third, combining it mind wandering is not per se maladaptive but becomes dysfunc- with simultaneous physiological assessment may ultimately help tional only when it takes the form of PC, we expect only the explain why MDD patients are at increased risk for cardiovascular latter to be associated with mood worsening. In light of the role disease, and finally, insights obtained from this type of assessment of vagal functioning as a hallmark of psychological and somatic may not only serve researchers and clinicians, but may also benefit health (Thayer and Lane, 2009),weexpecttofindanassociation patients directly (reviewed in Aan het Rot et al., 2012). between HRV during PC and psychopathological dispositions or A few studies investigated mind wandering with the use of eco- somatic complaints on the same day. Finally, we hypothesized logical momentary assessment (McVay et al., 2009; Killingsworth these effects to be potentiated in MDD patients compared to and Gilbert, 2010; Song and Wang, 2012; Unsworth et al., 2012; controls. Carriere et al., 2013), but only in healthy individuals and none of them simultaneously recorded physiological activity. Conversely, MATERIALS AND METHODS this has been widely done to study the physiological consequences PARTICIPANTS of rumination and worry (Brosschot et al., 2007; Pieper et al., The sample was composed of 18 subjects [12 women, 6 men; 2007, 2010; Slatcher et al., 2010; Ottaviani et al., 2011; Weise et al., mean age = 38.4 (12.1) years] who met diagnostic criteria for 2013). However, existing studies focused only on healthy indi- a current major depressive episode and 18 healthy controls [11 viduals. Overall, results show a sustained physiological activation women, 7 men; mean age = 30.1 (10.5) years], 13 Asian, 13 across several biological systems in association with rumina- Caucasian, 3 African, and 7 Latino Americans. Participants were tion and worry. The few studies that have been conducted in recruited by the use of flyers and participation in previous studies. MDD patients combining ecological momentary assessment with Exclusionary criteria were: being younger than 18, a diagno- concomitant physiological recording have so far mostly focused sis of psychotic or personality disorders, current active suicidal on cortisol. Together, results point toward a dysfunction of the ideation or intent, a diagnosis of heart disease or other seri- hypothalamic-pituitary-adrenal system in responding to daily ous medical illness, use of drugs/medications that might affect hassles in MDD patients (Peeters et al., 2003a, 2004). Huffziger HR and HRV, obesity (body mass index > 32 kg/m2), or preg- et al. (2013) found reduced mood-cortisol coupling in remitted nancy. Participants were compensated ($35) for their time. The

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experimental protocol was approved by the UCLA Institutional of the thought (5 min, 10 min, 20 min or since the last beep); (3) if Review Board. the thought was Positive, Negative, or Neutral; (4) if the thought was repetitive (from 0 = Not at all to 4 = Very much); (5) the PROCEDURE temporal dimension of the thought (Past, Future, Present); (6) After a pre-screening phone interview to rule out exclusion- if the thought was interfering with what they were doing (from ary criteria, participants came to the lab, read and signed the 0 = Not at all to 4 = Very much); (7) how much they were try- informed consent form, and underwent either the Structured ingtosuppressthethought(from0= Notatallto4= Ver y Clinical Interview for DSM-IV (SCID) or the Mini International much); (8) if they had experienced one or more annoying or Neuropsychiatric Interview (M.I.N.I), administered by a trained disturbing events in the preceding period (Yes/No); (9) informa- clinician to confirm the diagnosis of MDD. If eligible, participants tion on factors that may affect HR, including posture, physical filled out the online questionnaires and were then instructed activity, and food, caffeine, nicotine, and alcohol consumption about the cell phone used for the electronic diary and the ambu- since the last diary report; and (10) their current levels of feel- latory HR device. The ambulatory device was attached to their ing Tired, Anxious, Sad, Happy, Angry, and Bored using a 5-point chest, and they left the laboratory. After approximately 24 h, scale from Not at all to Very much. The diary included questions participants were asked to return the ambulatory device and about social interaction but these data go beyond our present the phone. On that occasion, they completed a post-assessment aim and will not be presented. Based on their content, valence, online questionnaire (data not presented here), were debriefed, temporal dimension, and repetitiveness, thoughts were re-coded and received monetary compensation. as: being on task, mind wandering (MW), and ruminating or worrying (PC). The exact coding flowchart is available from the QUESTIONNAIRES first author, upon request. By using the touch screen, responses During the first laboratory session, participants completed on could be made in a few minutes. Before bedtime, subjects were line a series of socio-demographic (age and sex) and lifestyle asked to fill out the Patient Health Questionnaire (PHQ-15 for (nicotine, alcohol, and caffeine consumption, physical exercise) somatization; Kroenke et al., 2002) and, upon awakening, the questions and questionnaires to measure levels of: (a) hostility PROMIS Sleep Disturbance-Short Form (Yu et al., 2011), both (CM; Cook and Medley, 1954), (b) anger (STAXI; Spielberger implemented on the same Android phone. et al., 1985), (c) trait anxiety (STAI-X2; Spielberger et al., 1970), (d) depression (PHQ-9; Kroenke et al., 2001 and BDI; Beck et al., AMBULATORY SESSION 1996), and (e) Loneliness (Three-Item Loneliness Scale; Hughes HR was recorded as beat-to-beat intervals in ms with the RS- et al., 2004). Three forms of PC were assessed: (1) depressive 800CX (Polar Electro) and the Bodyguard 2 (Firstbeat) HR moni- rumination (RRS; Nolen-Hoeksema and Morrow, 1991), mea- tors that have been extensively used for HR recording and analysis sured by how often people engage in responses to depressed mood (e.g., Porto and Junqueira, 2009). First, each diary entry was that are self-focused (e.g., I think “Why do I react this way?”), labeled in the cardiac data. Then, the 24-h raw beat-to-beat inter- symptom-focused (e.g., I think about how hard it is to concen- vals were arranged in 1 and 5-min blocks. HRV was assessed by trate), and focused on the possible consequences and causes of computing the root mean square of successive beat-to-beat inter- one’s mood (e.g., I think “I won’t be able to do my job if I don’t val differences (RMSSD), which reflects vagal regulation of HR snap out of this”); (2) worry (PSWQ; Meyer et al., 1990), that (Task Force, 1996). Outlier and artifact detection as well as HRV is mainly focused on future outcomes (e.g., “As soon as I finish analyses were performed using Kubios HRV software (Tarvainen one task, I start to worry about everything else I have to do”) et al., 2014). During waking hours, the 1-min epochs were fur- and therefore prevalent in anxiety disorders but also present in ther averaged based on subject’s report of the duration of MW or MDD; and (3) reactive rumination (SRRS; Robinson and Alloy, PC (5-, 10-, 20-, and 30-min). For sleep, the 1-min epochs were 2003), as a measure of the tendency to ruminate after the occur- further averaged based on self-reports of bed and wake up times. rence of stressful events (i.e., not confounded with depressive symptoms). The SRRS has three subscales: Negative Inferential STATISTICAL ANALYSES Style “Ruminate about how the stressor will affect other areas of Data are expressed as means (SD). Laboratory data process- your life,” Hopelessness “Think about the possibility that things ing and data analyses were performed with Systat 12.0 (Systat will never get better,” and Active Problem-Solving “Try to find Software Inc., Richmond, CA). As the distribution of HRV was something positive in the situation or something you’ve learned.” non-normal, this variable was log transformed. To evaluate the effects of socio-demographic factors on ELECTRONIC DIARY the dependent variables, Pearson correlations were performed Participants were provided with an electronic diary implemented between BMI, age, years of education, income, physical activity, on an Android phone. At variable times (about every 30 min), alcohol, and nicotine consumption, and HR and HRV during the phone alarm signaled participants to complete the diary. Each waking hours. Gender differences were evaluated by t-tests. diary asked them to report: (1) what they were thinking about DifferencesinnumberofepisodesofPC,meanHRandHRV (About what you are doing, About a problem you have, About a during wake and sleep, and socio-personality factors between the past experience, About something that may happen in the future, two groups were evaluated by t-tests and chi-square tests. Planning something, About someone you know, About your feel- Random effects regression models are the most appropriate ings or mood, Having a passing thought, Other); (2) the duration methods of analysis for repeated measures of HR, HRV, and

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diary variables (Shapiro et al., 2001). The Restricted Maximum Table 1 | Group differences for the main variables of the study. Likelihood method was used for model estimation. The covari- MDD (n = 18) HC (n = 18) p ance model among observations within subject was a random intercept plus Autoregressive model. This approach is particularly Gender 12F; 6M 11F; 7M 0.73 suitable, as the periodicity of thoughts, mood, and physiologi- Age (years) 38.4 (12.1) 30.1 (10.5) 0.03 cal measurement is likely to be highly heterogeneous, and it also Education 6C; 1D; 5G; 6P 4C; 2D; 5G; 7P 0.85 deals with missing values. Differences in sample sizes are due Employed 12N; 6Y 3N; 15Y 0.002 to missing values for some of the variables. Because it models BMI (Kg/m2) 27.4 (6.7) 22.4 (3.0) 0.01 each participant as a random effect, using this procedure accom- Ethnicity 3AA; 5AS; 8C; 2L 0AA; 8AS; 5C; 5L 0.13 modates interindividual variation in thoughts-mood–HR/HRV Smoking 15N; 3Y 18N; 0Y 0.07 relationships. Alcohol consumption 13N; 5Y 16N; 2Y 0.21 × The specific cognitive state, group, and group cognitive state Caffeine consumption 4N; 14Y 11N; 7Y 0.02 interaction were entered as predictors of each dependent variable: Exercise 5N; 13Y 4N; 14Y 0.70 efforts to inhibit, interference with ongoing activities, HR, HRV, STAXI 19.5 (5.1) 15.6 (5.8) 0.04 and mood. The biobehavioral variables that had a significant rela- BDI 31.1 (12.2) 6.7 (8.6) <0.0001 tionship with a given dependent variable and the occurrence of CM 30.5 (22.3) 18.1 (6.5) 0.03 stressful episodes were also entered. STAI 59.5 (8.8) 35.2 (11.5) <0.0001 Spearman’s correlations were performed to test the associ- PSWQ 55.0 (14.7) 48.7 (13.3) 0.19 ations between HRV during PC and (a) somatic complaints SRRS-PS 356.4 (100.1) 443.7 (154.1) 0.05 during the same day and (b) dispositional characteristics (SRRS, SRRS-NIS 586.6 (120.7) 373.9 (218.3) 0.001 CM,RRS,STAXI,PHQ-9,BDI,andPSWQ).Asourparticipants SRRS-H 302.7 (109.9) 83.7 (94.5) <0.0001 were not asked to fill out the diary at night, only the associa- PHQ-9 17.9 (6.1) 2.4 (3.2) <0.0001 tion between HRV during sleep and dispositional characteristics RRS 61.4 (8.9) 34.2 (9.9) <0.0001 (SRRS, CM, RRS, STAXI, PHQ-9, BDI, and PSWQ) was assessed Three-Item 7.0 (1.8) 4.6 (1.8) <0.0001 by Spearman’s correlations. Loneliness Scale To correct for multiple comparisons, Bonferroni corrected Wake HR (bpm) 85.3 (8.2) 77.6 (7.9) 0.01 p-values are presented. Wake RMSSD (ms2) 34.9 (16.9) 46.3 (15.3) 0.04 Sleep HR (bpm) 65.7 (8.3) 60.2 (9.4) 0.09 RESULTS Sleep RMSSD (ms2) 41.7 (18.9) 62.8 (33.5) 0.04 The only significant associations that emerged between socio- Stressful event (n) 2.7 (2.8) 1.3 (1.7) 0.07 demographic variables and outcome measures were between Frequency of MW (n) 4.4 (3.2) 7.5 (5.5) 0.05 HR and caffeine consumption (r = 0.34; p = 0.04) and between Frequency of PC (n) 4.5 (2.5) 2.4 (3.3) 0.04 HRV and age (r =−0.40; p = 0.02) and nicotine consumption PROMIS 10.4 (3.1) 12.0 (4.3) 0.22 (r =−0.38; p = 0.02). Thus, these variables were included in the Hours of sleep 6.70 (1.3) 7.26 (1.3) 0.21 appropriate regression model. No gender differences emerged, Time to sleep 25.18 (23.9) 15.28 (14.4) 0.14 except for a tendency toward lower HRV in women during sleep PHQ-15 8.7 (5.6) 3.0 (4.4) 0.002 (t = 2.0; p = 0.05). Table 1 shows group differences for the main variables of the M, Males; F, Females; C, College; D, Diploma; G, Graduate; P, Postgraduate; Y, study. Compared to healthy controls, MDD participants were Yes; N, No; AA, African American; AS, Asian; C, Caucasian; L, Latino; SRRS-PS, Problem Solving subscale of the SRRS; SRRS-NIS, Negative Inferential subscale older (t = 2.2), had higher BMI (t = 2.9), had more episodes of of the SRRS; SRRS-H, Hopelessness subscale of the SRRS. PC (t = 2.2), and higher scores on the STAXI (t = 2.1), BDI (t = 6.9), CM (t = 2.3), STAI (t = 7.1), negative inferential style and hopelessness subscales of the SRRS (t = 3.6andt = 6.4, respec- HR compared to controls [F(1, 563) = 7.20; p = 0.01], irrespec- tively), PHQ-9 (t = 9.5), PHQ-15 (t = 3.4), RRS (t = 8.6), and tive of their ongoing cognitive state. However, cognitive state was Loneliness scale (t = 4.0). More MDD participants consumed a significant predictor of HRV (see Figure 2), showing that PC 2 caffeine (χ = 5.6) and they were less likely to be employed was associated with a lower HRV compared to MW [F(2, 562) = (χ 2 = 9.2). Compared to controls, MDD participants showed 12.28; p < 0.0001], irrespective of group. Moreover, group played higher HR during wake (t = 2.9) and lower HRV during wake a significant role in this model [F(1, 562) = 12.68; p < 0.0001], and sleep (Table 1; t =−2.1and−4.2, respectively). with MDD participants being characterized by lower HRV. Age As to the random regression models, in both groups thoughts [F(1, 563) = 6.6; p = 0.01] and nicotine [F(1, 563) = 21.05; p < required more effort to be inhibited and interfered more with 0.0001] were also significant predictors in the model. ongoing activities during PC compared to MW (see Figure 1), As to mood (see Figure 3), group was significant in the as indicated by the main effect of cognitive state in these mixed prediction of being tired [F(1, 564) = 4.66; p = 0.03], anx- regression models; F(1, 335) = 42.67; p < 0.0001 and F(1, 335) = ious [F(1, 564) = 20.75; p < 0.0001], sad [F(1, 564) = 13.83; p < 124.16; p < 0.0001, respectively. 0.0001], and bored [F(1, 564) = 10.57; p = 0.001], with MDD For the mixed model predicting HR, only group resulted sig- participants showing worse mood compared to healthy con- nificant as a predictor, with MDD participants having a higher trols. Cognitive state was significant predictor of being tired

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FIGURE 1 | Answers to the diary questions “Was the thought interfering with what you were doing?” (interference) and “How much were you trying to suppress the thought?” (suppress) for the periods during which participants (MDD and HC) were engaged in MW or PC.

FIGURE 3 | Mean mood rating for the periods during which participants (MDD and HC) were engaged in MW, PC or were focused on task (T).

mixed models predicting happy [F(2, 564) = 6.40; p = 0.002] and bored [F(2, 564) = 17.21; p < 0.0001], with healthy controls being less happy and more bored during PC compared to MW and being on task and MDD patients being equally happy and bored irrespective of the ongoing cognitive process. HRV during PC signiﬁcantly correlated with somatic complaints on the same day, as assessed by the PHQ-15 (rs =−0.31) and with the following dispositional characteristics: hopelessness FIGURE 2 | Mean ambulatory HRV for the periods during which =− . =− . participants (MDD and HC) were engaged in MW, PC or were focused rumination (SRRS; rs 0 42), trait anxiety (STAI; R 0 40), on task (T), adjusted for age and caffeine consumption. and depression (PHQ-9; rs =−0.39).HRVduringsleepwas negatively correlated with hopelessness rumination (rs =−0.32).

[F(2, 564) = 14.52; p < 0.0001], anxious [F(2, 564) = 60.61; p < DISCUSSION 0.0001], sad [F(2, 564) = 66.29; p < 0.0001], happy [F(2, 564) = Our main hypothesis that PC would be associated with higher lev- 23.14; p < 0.0001], angry [F(2, 564) = 35.18; p < 0.0001], and els of rigidity (cognitive, autonomic, and behavioral) compared to bored [F(2, 564) = 32.47; p < 0.0001], with PC being always char- MW was supported by the data. In line with our view that MW acterized by worse mood compared to MW and being on task. is not per se maladaptive, only PC was associated with health risk A signiﬁcant group x cognitive state interaction emerged for the factors (i.e., low HRV) and mood worsening. Several laboratory

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and ambulatory studies previously found reduced HRV during so-called ruminating mind. The idea that MW is not per se dys- rumination (e.g., Key et al., 2008; Ottaviani et al., 2008; Ottaviani functional in terms of health and mood consequences has been and Shapiro, 2011) and worry (e.g., Castaneda and Segerstrom, further supported by a recent longitudinal study conducted by 2004; Hofmann et al., 2005; Delgado et al., 2009; Brosschot et al., our group (Ottaviani and Couyoumdjian, 2013). 2007; Ottaviani et al., 2014) in healthy participants. On the other As expected, MDD participants’ scores on all dispositional hand, psychophysiological studies of MW did not include HRV. questionnaires were notably higher than scores for healthy con- To the best of our knowledge, only Ottaviani et al. (2013) directly trols. Aside from depression, MDD participants felt more lonely, compared PC and MW in terms of physiological and affective had higher levels of anxiety, anger, hostility, and trait rumina- consequences. Here, we replicated and extended our previous tion, the latter specifically focused on depressive symptoms and findings in two ways: (1) from the laboratory to daily life and (2) characterized by a negative inferential style and hopelessness. The from healthy participants to patients with a diagnosis of MDD. same results on anxiety, anger, and hostility have been previously In general, MDD participants had higher HR and lower HRV reported by our group (Davydov et al., 2007). Other authors con- compared to healthy controls. This confirms previous results firmed the association between subclinical and clinical depression (e.g., Koschke et al., 2009) and has important clinical implica- and loneliness (e.g., Wei et al., 2005), anger (e.g., Besharatetal., tions considering that (1) low HRV has been associated with 2013), and hostility (e.g., Conrad et al., 2009). The relationship increased risk of all-cause mortality (Thayer et al., 2010)and(2) between rumination and MDD is one of the most well-replicated in a recent multi-treatment study, Brunoni et al. (2013) showed finding in this field (e.g., Nolen-Hoeksema et al., 2008). In fact, that reduced HRV is a trait-marker for MDD and not only a mere rumination has been linked to longer and more severe depression, consequence of pharmacotherapy. Moreover, PC was character- delayed recovery from depression, increases in suicidal ideation ized by HRV reductions in both MDD and healthy participants (e.g., Eshun, 2000; Lyubomirsky and Tkach, 2004; Smith et al., compared to MW. This autonomic rigidity was mirrored by cog- 2006), and has also prospectively predicted major depression over nitive and behavioral inflexibility, as reflected by higher efforts a 2.5-year follow up (Nolen-Hoeksema, 2000; Spasojevic and to inhibit the thought and interference with ongoing activities Alloy, 2001). during PC compared to MW. This supports the notion that Compared to healthy controls, MDD participants were not HRV may index important organism functions associated with characterized by higher dispositional worry. Even if it seems intu- adaptability and flexibility, as it indices the degree of functional itive that worrisome thoughts about future threats may better integration between the ventro-medial prefrontal cortex, brain- correlate with anxiety than depression, this is in disagreement stem, and peripheral physiology (Thayer and Lane, 2009; Thayer with findings in which worry and rumination did not differ- et al., 2012). entially relate to anxiety or depression (Segerstrom et al., 2000; Irrespective of the ongoing cognitive process, worse mood was Hong, 2007). However, inconsistencies may be explained by the reported by MDD patients compared to controls, except for being fact that previous studies focused on healthy individuals (i.e., stu- angry. This is consistent with a number of ambulatory studies in dents), while our study was conducted on a clinical population. which MDD patients reported less positive affect and more nega- Indeed, the specificity of worry and rumination as vulnerability tive affect than healthy controls (e.g., Peeters et al., 2003b; Bylsma factors for the development of anxiety and depressive symptoms et al., 2011). in children has been more recently reported (Verstraeten et al., Both groups were more anxious, sad, tired, and angry during 2011). PC compared to MW and being on task, again replicating pre- Surprisingly, no difference emerged between the pathological viousresults(Ottaviani et al., 2013). The effects of PC on mood and healthy samples in terms of quality of sleep. This is con- provide further support to Nolen-Hoeksema’s (2004) Response trary to previous studies in which depression and greater levels Style Theory which suggests that rumination negatively impacts of intrusive thoughts were associated with both subjective and individuals through the activation of negative thoughts and mem- laboratory-assessed sleep disruptions (Hall et al., 1997; Kecklund ories, exacerbating the impact of depressed mood on thinking and and Akerstedt, 2004). We had to rely on subjective reports, which increasing the likelihood that individuals will make depressogenic may have limited the reliability of results. However, our more inferences in regard to their current circumstances. In our study, objective marker during sleep (HRV) showed significant differ- MW did not have different effects in terms of mood worsening ences between the two groups and was negatively correlated with comparedtobeingontask,neitherinhealthynorinMDDpartic- hopelessness rumination. This provides support to the few ambu- ipants. This is in contrast with others’ findings (e.g., Killingsworth latory studies that highlighted that -at least at a physiological and Gilbert, 2010), but again, previous studies did not directly level—the consequences of PC extend to the following night, as compare MW and PC, and probably included both constructs shown by reductions in HRV during sleep (Brosschot et al., 2007; under their definition of MW. In our opinion, it is very impor- Yoshino and Matsuoka, 2009; Weise et al., 2013). tant to understand when spontaneous thought is functional (e.g., In agreement with previous data (e.g., Haug et al., 2004), MDD for creativity and insights) and when it is not, especially for mood participants reported more somatic symptoms (e.g., headaches, disorders. In their recent theoretical model, Tops et al. (2014) stomach pain) compared to controls during the ambulatory suggest that it is possible to make specific neuro-behavioral pre- assessment. These cross-sectional findings are experimentally dictions about adaptive and maladaptive cognitive states based on supported by the role of sad mood induction in increasing the theory of predictive and reactive control systems (Tops et al., pain sensitivity in MDD patients (Terhaar et al., 2010). Also, 2014), further proposing ad hoc therapeutic intervention for the the amount and entity of somatic complaints in our study was

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negatively correlated with HRV during rumination. The sub- received from the Italian Ministry of Health Young Researcher stantial effects of PC on somatic health have been repeatedly Grant (GR-2010-2312442) awarded to Cristina Ottaviani and demonstrated (reviewed in Verkuil et al., 2010). For example, the Gail and Gerald Oppenheimer Family Foundation (Leila non-clinical levels of worry were prospectively associated with Shahabi). The authors are grateful to Julian F. Thayer for kindly self-reported health complaints (Brosschot and Van Der Doef, lending the Bodyguard 2 (Firstbeat) heart rate monitor to us and 2006; Jellesma et al., 2009; Verkuil et al., 2012) and even a simple to undergraduate UCLA research assistants for their help in data worry intervention resulted effective in reducing health com- collection. plaints (Brosschot and Van Der Doef, 2006; Jellesma et al., 2009). 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